Data Analytics professional (B.Tech CSE – Data Science, 2026) with hands-on experience in SQL, Python, Power BI, and Tableau across analytics and business intelligence roles. Proven ability to collect, process, and analy…
Data Analytics professional (B.Tech CSE – Data Science, 2026) with hands-on experience in SQL, Python, Power BI, and Tableau across analytics and business intelligence roles. Proven ability to collect, process, and analyze large datasets, design decision-support dashboards, and present actionable insights to stakeholders. Actively targeting Data/Business Analyst roles with a long-term focus on financial analytics at top-tier investment banks.
<p>Developed Python-based predictive models (Bayesian Inference, Logistic Regression) to optimize healthcare resource allocation, improving donor identification efficiency by 2.3% over automated baselines.<br>
Designed and deployed interactive Power BI dashboards.<br>
Performed end-to-end data analysis.<br>
Presented comprehensive data stories to executive stakeholders, enabling data-driven operational decisions.</p>
<ul><li>Developed Python-based predictive models (Bayesian Inference, Logistic Regression) to optimize healthcare resource allocation, improving donor identification efficiency by 2.3% over automated baselines.</li><li>Designed and deployed interactive Power BI dashboards; performed end-to-end data analysis and presented comprehensive data stories to executive stakeholders, enabling data-driven operational decisions.</li></ul>
<p>Built an ML web application (Flask + Random Forest) to predict manufacturing productivity scores.<br>
Managed the full data pipeline including cleaning, encoding, and feature selection.<br>
Developed dashboards to translate raw production data into actionable efficiency metrics, supporting resource planning and process optimization.</p>
<ul><li>Built an ML web application (Flask + Random Forest) to predict manufacturing productivity scores and managed the full data pipeline including cleaning, encoding, and feature selection.</li><li>Developed dashboards to translate raw production data into actionable efficiency metrics, supporting resource planning and process optimization.</li></ul>
<p>Drove a 20% increase in user growth and 15% reduction in time-to-market by applying user behavior analysis, workflow automation, Agile methodology, and data-informed product strategy.<br>
Established a KPI framework and data-driven process improvements that reduced recruiter screening time by 60%, demonstrating strong business acumen and metrics reporting capability.</p>
<ul><li>Drove a 20% increase in user growth and 15% reduction in time-to-market by applying user behavior analysis, workflow automation, Agile methodology, and data-informed product strategy.</li><li>Established a KPI framework and data-driven process improvements that reduced recruiter screening time by 60%, demonstrating strong business acumen and metrics reporting capability.</li></ul>
<ul><li>Built Python pricing models for natural gas storage contracts and credit risk—applying time-series forecasting, logistic regression, and ensemble ML to estimate commodity prices and borrower probability of default (PD) with recovery-rate assumptions.</li><li>Engineered an optimal FICO score quantization model using dynamic programming (log-likelihood maximization) to categorize 10,000+ mortgage borrowers into monotonic risk-rated buckets, reducing default prediction error across a simulated loan portfolio.</li></ul>
<p>• Engineered an autonomous multi-agent system automating financial due diligence via parallel data ingestion pipelines, reducing preliminary research latency by 50% performing financial analysis.</p><p>• Integrated real-time market data (yFinance) with sentiment analysis to generate institutional-grade Buy/Hold/Sell investment memos; implemented Pydantic validation ensuring 100% data integrity for valuation metrics (P/E, Market Cap) across international equity markets.</p>
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